Multiview Deep Learning Framework for Precise Prediction of Transcription Factor Binding Sites.

Transcription factors (TFs) are essential proteins that regulate gene expression by specifically binding to transcription factor binding sites (TFBSs) within DNA sequences. Their ability to precisely control the transcription process is crucial for understanding gene regulatory networks, uncovering disease mechanisms, and designing synthetic biology tools. Accurate TFBS prediction, therefore, holds significant importance in advancing these areas of research.

Lung adenocarcinoma subtype classification based on contrastive learning model with multimodal integration.

Accurately identifying the stages of lung adenocarcinoma is essential for selecting the most appropriate treatment plans. Nonetheless, this task is complicated due to challenges such as integrating diverse data, similarities among subtypes, and the need to capture contextual features, making precise differentiation difficult. We address these challenges and propose a multimodal deep neural network that integrates computed tomography (CT) images, annotated lesion bounding boxes, and electronic health records.

BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models.

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we develop an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images.

Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.

Purpose: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to enhance early risk assessment and improve clinical management strategies.

Methods: This retrospective study included 243 patients with early-stage cirrhosis across two centers.

Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning.

This study aimed to develop and evaluate an automated classification model for diagnosing chronic hepatitis B (CHB) complicated with non-alcoholic fatty liver disease (NAFLD) using deep learning techniques applied to two-dimensional liver imaging. We retrospectively analyzed data from 2803 patients diagnosed with CHB and NAFLD via two-dimensional ultrasound and FibroScan at our hospital between June 2019 and December 2022. These patients contributed a total of 20,540 two-dimensional liver images.

Efficient substructure feature encoding based on graph neural network blocks for drug-target interaction prediction.

Background: Predicting drug-target interaction (DTI) is a crucial phase in drug discovery. The core of DTI prediction lies in appropriate representations learning of drug and target. Previous studies have confirmed the effectiveness of graph neural networks (GNNs) in drug compound feature encoding.

Reassessing the role of nitric oxide in the pathogenesis of sphincter of Oddi dysfunction.

The pathogenic mechanisms underlying sphincter of Oddi dysfunction (SOD) remain incompletely understood, and it often leads to severe symptoms encompassing nausea, vomiting, and abdominal pain. New evidence now suggests correlations between nitric oxide (NO) and SOD. In this review, we summarized the factors influencing SOD pathogenesis via NO and its derivative, the peroxynitrite anion.

MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction.

The discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein targets, focusing on binding within protein cavities. However, this manual process is slow and inherently limited.

Development and evaluation of USCnet: an AI-based model for preoperative prediction of infectious and non-infectious urolithiasis.

Background: Urolithiasis, a prevalent condition characterized by a high rate of incidence and recurrence, necessitates accurate preoperative diagnostic methods to determine stone composition for effective clinical management. Current diagnostic practices, reliant on postoperative specimen analysis, often fail to facilitate timely and precise therapeutic decisions, leading to suboptimal clinical outcomes. This study introduces an artificial intelligence model developed to predict infectious and non-infectious urolithiasis preoperatively using clinical data and CT imaging.

FOXM1, a super enhancer-associated gene, is related to poorer prognosis and gemcitabine resistance in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive solid tumor; however, the barrier of chemoresistance has yet to be overcome for longer survival. Aberrant gene expression due to epigenetic modification plays an important role in tumorigenesis and treatment. Super enhancers are epigenetic elements that promote targeted gene transcription and ultimately lead to chemoresistance.

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH.

Multimodal contrastive learning for spatial gene expression prediction using histology images.

In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, the prohibitive cost of ST technology remains a significant barrier to its widespread adoption in large-scale studies. An alternative, more cost-effective strategy involves employing artificial intelligence to predict gene expression levels using readily accessible whole-slide images stained with Hematoxylin and Eosin (H&E).

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing high-dimensional microimage data.

UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-Scale Generation and Registration Enhancement.

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts.

Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes.

TDFFM: Transformer and Deep Forest Fusion Model for Predicting Coronavirus 3C-Like Protease Cleavage Sites.

COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C-like protease of the coronavirus (denoted as 3CL) is instrumental in the viral replication process.

Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases.

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients' blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis.

MiR-26a-5p exerts its influence by targeting EP300, a molecule known for its role in activating the PI3K/AKT/mTOR signaling pathway in CD8+tumor-infiltrating lymphocytes of colorectal cancer.

Surgical resection remains the primary approach for treating colorectal cancer, which is among the prevalent types of cancers affecting the digestive system. Tumor-infiltrating lymphocyte (TIL) therapy has emerged as a prominent area of study in the field of tumor immunotherapy in recent times, with the potential to serve as a supplementary treatment for colorectal cancer. For this investigation, we employed single-cell sequencing data to assess the manifestation extent of miR-26a-5p exists in healthy colon tissue, tissue affected by colorectal cancer, and tissue adjacent to the tumor.

Exploring the mechanism of resistance to vincristine in breast cancer cells using transcriptome sequencing technology.

Breast cancer has replaced lung cancer as the leading cancer globally, but various chemotherapy drugs for breast cancer are prone to resistance, especially in patients with distant metastases who are susceptible to multiple chemotherapy drug resistance often leading to treatment failure. Vincristine (VCR) is an alkaloid extracted from , and is often used in combination with other chemotherapy drugs to treat various types of cancer, including breast cancer. Research on the development of resistance to VCR has been carried out using transcriptome sequencing technology.

Characterization of anoikis-based molecular heterogeneity in pancreatic cancer and pancreatic neuroendocrine tumor and its association with tumor immune microenvironment and metabolic remodeling.

Background: Accumulating evidence suggests that anoikis plays a crucial role in the onset and progression of pancreatic cancer (PC) and pancreatic neuroendocrine tumors (PNETs); nevertheless, the prognostic value and molecular characteristics of anoikis in cancers are yet to be determined.

Materials And Methods: We gathered and collated the multi-omics data of several human malignancies using the TCGA pan-cancer cohorts. We thoroughly investigated the genomics and transcriptomics features of anoikis in pan-cancer.

Determination and characterization of molecular heterogeneity and precision medicine strategies of patients with pancreatic cancer and pancreatic neuroendocrine tumor based on oxidative stress and mitochondrial dysfunction-related genes.

Background: Mitochondria are significant both for cellular energy production and reactive oxygen/nitrogen species formation. However, the significant functions of mitochondrial genes related to oxidative stress (MTGs-OS) in pancreatic cancer (PC) and pancreatic neuroendocrine tumor (PNET) are yet to be investigated integrally. Therefore, in pan-cancer, particularly PC and PNET, a thorough assessment of the MTGs-OS is required.

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

Background: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach.

Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning.

Background: RNA-binding proteins (RBPs) are crucial factors that function in the posttranscriptional modification process and are significant in cancer.

Objective: This research aimed for a multigene signature to predict the prognosis and immunotherapy response of patients with colon adenocarcinoma (COAD) based on the expression profile of RNA-binding proteins (RBPs).

Methods: COAD samples retrieved from the TCGA and GEO datasets were utilized for a training dataset and a validation dataset.